Volgenau School of Engineeringhttp://hdl.handle.net/1920/4662018-03-19T14:58:01Z2018-03-19T14:58:01ZQuantifying impacts of upstream reservoirs on the Potomac River due to Consumptive UseLanza, Beverlyhttp://hdl.handle.net/1920/107052017-05-25T06:31:42Z2017-05-01T00:00:00ZQuantifying impacts of upstream reservoirs on the Potomac River due to Consumptive Use
Lanza, Beverly
The Potomac River basin is home to more than six million residents and has experienced moderate and severe droughts in the past (e.g., 1930, 1966, 1999, and 2002). With population growth and consequential increasing water demands, net water withdrawals by upstream users can impact the water supplies for downstream populations, as for example in the Washington Metropolitan Area (WMA). Three reservoirs, Jennings Randolph, Savage, and Little Seneca, are part of the WMA water supply system, and are used to augment Potomac River flow during droughts. This study focuses on investigating if upstream reservoirs which are not part of the WMA system partially mitigate the impacts of Upstream Consumptive Use (CU) on the Potomac River. Therefore, a GIS inventory of the ten largest reservoirs located in West Virginia, Pennsylvania, Maryland, and Virginia was created. The GIS database includes the following information: location, owner, storage capacity, reservoir inflows, and current water supply demands. Each parameter listed above was analyzed along with a safe yield calculation for each reservoir. All of this information is useful and must be considered in order to properly determine the impacts upstream reservoirs. Therefore, through this study we aim to support the planning and management of water resources in the region.
2017-05-01T00:00:00ZTime-Dependent Increase in Network Response to StimulationHamilton, FranzGraham, RobertLuu, LydiaPeixoto, Nathaliahttp://hdl.handle.net/1920/100232015-11-18T20:22:31Z2015-11-06T00:00:00ZTime-Dependent Increase in Network Response to Stimulation
Hamilton, Franz; Graham, Robert; Luu, Lydia; Peixoto, Nathalia
In vitro neuronal cultures have become a popular method with which to probe network-level neuronal dynamics and phenomena in controlled laboratory settings. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Here we demonstrate the effects of a high frequency electrical stimulation signal in training cultured networks of cortical neurons. Networks receiving this training signal displayed a time-dependent increase in the response to a low frequency probing stimulation, particularly in the time window of 20–50 ms after stimulation. This increase was found to be statistically significant as compared to control networks that did not receive training. The timing of this increase suggests potentiation of synaptic mechanisms. To further investigate this possibility, we leveraged the powerful Cox statistical connectivity method as previously investigated by our group. This method was used to identify and track changes in network connectivity strength.
2015-11-06T00:00:00ZEffective Automated Feature Construction and Selection for Classification of Biological SequencesKamath, UdayDe Jong, KennethShehu, Amardahttp://hdl.handle.net/1920/98282015-09-15T04:21:18Z2014-07-17T00:00:00ZEffective Automated Feature Construction and Selection for Classification of Biological Sequences
Kamath, Uday; De Jong, Kenneth; Shehu, Amarda
Background
Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.
Methodology
We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.
Results
To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTool​s.
2014-07-17T00:00:00ZA Novel Application of Musculoskeletal Ultrasound ImagingEranki, AvinashCortes, NelsonFerenček3, Zrinka GregurićSiddhartha, Sikdarhttp://hdl.handle.net/1920/88152015-06-02T19:28:28Z2013-09-01T00:00:00ZA Novel Application of Musculoskeletal Ultrasound Imaging
Eranki, Avinash; Cortes, Nelson; Ferenček3, Zrinka Gregurić; Siddhartha, Sikdar
Ultrasound is an attractive modality for imaging muscle and tendon motion during dynamic tasks and can provide a complementary methodological approach for biomechanical studies in a clinical or laboratory setting. Towards this goal, methods for quantification of muscle kinematics from ultrasound imagery are being developed based on image processing. The temporal resolution of these methods is typically not sufficient for highly dynamic tasks, such as drop-landing. We propose a new approach that utilizes a Doppler method for quantifying muscle kinematics. We have developed a novel vector tissue Doppler imaging (vTDI) technique that can be used to measure musculoskeletal contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities using ultrasound. The goal of this preliminary study was to investigate the repeatability and potential applicability of the vTDI technique in measuring musculoskeletal velocities during a drop-landing task, in healthy subjects. The vTDI measurements can be performed concurrently with other biomechanical techniques, such as 3D motion capture for joint kinematics and kinetics, electromyography for timing of muscle activation and force plates for ground reaction force. Integration of these complementary techniques could lead to a better understanding of dynamic muscle function and dysfunction underlying the pathogenesis and pathophysiology of musculoskeletal disorders.
2013-09-01T00:00:00Z